drbaph / FireRedTTS3-int8

FireRedTTS3-int8 (community INT8 ConvRot mirror)

INT8 ConvRot conversion of FireRedTeam/FireRedTTS3 for FireRedTTS3-ComfyUI , produced with the official comfy-kitchen quantizer ( TensorWiseINT8Layout.quantize , registry quantize_int8_convrot_weight ).

Format per quantized Linear (current ComfyUI representation):

• weight — torch.int8 , original [out, in] shape, contains the offline Hadamard-rotated weight ( W @ H^T per 256-column group)
• weight_scale — torch.float32 , [out, 1] per-output-row scale
• bias — original float bias
• comfy_quant — uint8 JSON: {"format": "int8_tensorwise", "convrot": true, "convrot_groupsize": 256}

At inference the companion custom node rotates activations online via comfy_kitchen.int8_linear(..., convrot=True, convrot_groupsize=256) — dynamic per-row INT8 activation quantization + INT8 GEMM, rescaled by scale_x * scale_w . No whole-weight dequantization on the hot path.

What is quantized (safe profile, group size 256)

Sizes

Validation (base variant, full suite; instruct smoke-tested)

• Per-layer weight roundtrip (official quantize -> official dequantize): worst rel-L2 0.00967 , worst cosine 0.999953 over 321 layers
• Real-activation comparison vs fp32 through the same comfy_kitchen.int8_linear runtime: worst rel-L2 0.01162 , worst cosine 0.999932
• On-disk structure: all 677 original keys preserved, scales fp32 [N,1] and positive, Conv1d/RedAE/CAM++ untouched
• Runtime proof: 321 ConvRotInt8Linear modules, >42k counted INT8 ConvRot kernel calls during generation, weights stay int8 across unload/reload
• BF16 vs INT8 generation (same seed/settings): identical patch counts (200/200), finite latents, EN/ZH ASR-verified, speaker-similarity parity (0.9007 vs 0.8991)
• Peak VRAM 13.1 -> 8.3 GiB; generation ~1.3x slower (memory optimization, honestly reported)

Usage Disclaimer

• The project incorporates zero-shot voice cloning functionality; Please note that this capability is intended solely for academic research purposes .
• DO NOT use this model for ANY illegal activities ❗️❗️
• The developers assume no liability for any misuse of this model.
• If you identify any instances of abuse , misuse , or fraudulent activities related to this project, please report them to our team immediately.

Citation

@article{fireredtts3,
  title   = {FireRedTTS3: Unified Speech Generation and Editing with Semantically Enriched Speech Representations},
  author  = {FireRed Team},
  journal = {arXiv preprint},
  year    = {2026},
}

Acknowledgements

• Qwen3 and Qwen2-Audio for the language model and audio understanding foundations
• DiTAR for the patch-level diffusion autoregressive formulation
• X-Codec for the discriminator design used in RedAE training
• CAM++ for speaker embedding extraction
• fastText for automatic language identification
• WeTextProcessing (wetext) for the Chinese / English text normalization front-end

All credit to the FireRed Team — see the upstream repo and model card. Apache-2.0.